code: def test_reduction_method(tweet_emotion_train_test_textdata_sampled):
      # Arrange
      train, test = tweet_emotion_train_test_textdata_sampled
      check = TextEmbeddingsDrift(dimension_reduction_method="PCA")
      # Act
      result = check.run(train, test)
  
      assert_that(
          result.value["domain_classifier_drift_score"],
          close_to(0.11, 0.01),
      )
  
      # Make sure uses PCA with auto + with_display false:
      check = TextEmbeddingsDrift(dimension_reduction_method="auto")
      # Act
      result = check.run(train, test, with_display=False)
  
      assert_that(
          result.value["domain_classifier_drift_score"],
          close_to(0.11, 0.01),
      )
  
      # Make sure doesn't use embeddings if none:
      check = TextEmbeddingsDrift(dimension_reduction_method="none")
      # Act
      result = check.run(train, test)
  
      assert_that(
          result.value["domain_classifier_drift_score"],
          close_to(0.18, 0.01),
      )
  
file_path: deepchecks/tests/nlp/checks/train_test_validation/text_embeddings_drift_test.py
imports: from sys import platform
  import numba
  from hamcrest import assert_that, close_to
  from deepchecks.nlp.checks import TextEmbeddingsDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_reduction_method(tweet_emotion_train_test_textdata_sampled) without return types